Tag: YouTube

YouTube‘s become a verb and a household name, but I’ll always see it as an organization that’s brought metrics into the lives of the common people (those who have broadband Internet, anyway). The “Most Popular” and “Featured Videos” are seen worldwide, sometimes garnering millions of views. “Hey, did you see….” is usually accompanied by something like “…and it has x million views on YouTube!”

Number of views is great for little else other than bragging rights. It’s one of the “famous” metrics (web analytics guru Avinash Kaushik‘s term) that “are staring you in the face when you crack open any analytics tool” but “barely contain any insight.”

Yep, for anyone in the content business, number of views is right up there with hall of famers number of page views and monthly unique visitors.

YouTube has pushed all of its account holders – no matter how amateur – to use meaningful metrics. In March 2008 it launched Insight, its “video analytics tool for all users,” along with some almost-preachy instructions on how to use metrics to get more people to watch your videos and, of course, come more often to YouTube.

The Insight tool allows you to track “community engagements” (there’s that word again) in terms of ratings, comments, and favorites. YouTube doesn’t want you to settle for people just watching your video. People have to show, in a measurable way, that they not only watched it but also reacted to it.

At the very least people should give a star rating (one is bad, five is good). Rating is easy, quick and anonymous. Tagging a video as a favorite is the next rung. And if they’re really engaged, they’ll leave comments.

But, as anyone who’s ever spent any time at all on YouTube knows, many comments are spam, obscene and irrelevant – just noise. But the value of social media metrics is in looking beyond what James Kobelius in Information Management points out is an “often low and laughable” signal-to-noise ratio.

Kobelius notes that “if you crawl, correlate, categorize, mine, and explore it with the
right tools….[this unstructured information] can yield unexpected insights….The intelligence value of any individual tweet [or comment] in isolation is
negligible….Intelligence emerges from the aggregate.”

If you can stomach a few obscenities, look at this thought in Encyclopaedia Dramatica about YouTube view fraud and how the ratio of VPC, or views per comment, “is the most accurate way to determine if anyone” cares. “A high VPC usually means view fraud has been committed.”

The example in ED shows that a video with 136,097 views and 3,529 comments has a VPC of 38.7, a low number that indicates this is a video “that people actually find funny.” The video with 296,413 views, 541 comments and thus a VPC of 547.9 is probably something nobody really cares about.

I calculated some VPCs from this week’s “Most Popular” videos and came up with some numbers that I don’t know what to do with yet. To see if VPC can be used as a key performance indicator, I’ll need to calculate VPCs and crawl through the cacophony of a variety of news videos. VPC may never be “famous,” but it might be insightful.